CN104615658A - Method for confirming user identity - Google Patents
Method for confirming user identity Download PDFInfo
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- CN104615658A CN104615658A CN201410855473.XA CN201410855473A CN104615658A CN 104615658 A CN104615658 A CN 104615658A CN 201410855473 A CN201410855473 A CN 201410855473A CN 104615658 A CN104615658 A CN 104615658A
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
- G06F16/35—Clustering; Classification
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/90—Details of database functions independent of the retrieved data types
- G06F16/95—Retrieval from the web
- G06F16/958—Organisation or management of web site content, e.g. publishing, maintaining pages or automatic linking
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Abstract
The invention provides a method for confirming a user identity. The method comprises the step of obtaining log data related to user operation; confirming an operation feature of each user based on the obtained log data; dividing the users into groups based on the operation features, wherein users of the same or similar operation features are classified into one group; obtaining the identity of one user in each group; confirming the identity of the one user as the identity of the user in the group where the one user is located in.
Description
Technical field
The present invention relates to magnanimity microcomputer data processing, particularly relate to a kind of method based on daily record data determination user identity.
Background technology
Existing user identity identification side mainly contains two kinds: a kind of is the identity being determined user by business personnel's hand, and business personnel directly takes identity data from the business partner hand of cooperating with each other, and forms identity database; Another kind is determined one's identity from main separation by user, arranges some identity options, then, guides user from main separation identity before user uses product.
All there is obvious shortcoming and defect in these two kinds of methods.Business personnel's hand determines one's identity method efficiency very lowly, only relies on and completes identity validation by hand, also no longer may after customer volume is increased to very large magnitude; And owing to often there is the situation that a multiple people of account uses simultaneously, rely on business personnel to be merely able to determine one of them, automatically cannot find other sub-accounts, cause identity validation not comprehensive; In addition, hand confirmation method is very difficult for the renewal of identity data, and each user identity changes all needs business personnel's time update, ageing very poor.Equally also there is shortcomings from the method for main separation identity in user, the participation of the user of the method is lower, and a lot of user is unwilling to confirm oneself identity; It two is that data reliability is poor, and user often makes a false report identity for the phychology of self-protection.
Therefore prior art also existing defects, urgently improves.
Summary of the invention
For overcoming the deficiencies in the prior art, the invention provides a kind of method determining user identity.
According to an aspect of the present invention, provide a kind of method determining user identity, described method comprises: obtain the daily record data relevant to user operation; According to the daily record data obtained, determine the operating characteristics of each user; According to operating characteristics, group is divided to user, wherein, user identical or similar for operating characteristics is divided to same cohort; Obtain the identity of a user in each cohort; The identity of a described user is defined as the identity belonging to user of the cohort at a described user place.
Preferably, the described daily record data relevant to user operation at least comprises following information: the mark of user, the action type of user, the operation mark of user and the running time of user.
Preferably, describedly determine that the step of the operating characteristics of each user comprises: from the daily record data obtained, extract the operation information of each user; Based on the operation information extracted, build the digraph of the operation of each user; From the digraph built, extract the feature of the digraph of the operation of each user; The feature of the digraph of the operation of each user extracted is defined as the operating characteristics of each user.
Preferably, the step of the digraph of the operation of each user of described structure comprises: determine the node corresponding with the operation information extracted for same user; Line between two nodes operation existing predetermined priority and perform relation is defined as directed edge, and the running time of this directed edge node comparatively early points to running time of this directed edge more late node; Based on the node determined and directed edge, build the digraph of the operation of this user.
Preferably, between any two nodes running time, interval was not more than first threshold time, there is predetermined priority and perform relation in the operation determining between described two nodes.
Preferably, the feature of described digraph at least comprises one of following information: the limit number of the out-degree of digraph, the in-degree of digraph, digraph and the node number of digraph.
Preferably, described step user identical or similar for operating characteristics being divided to same cohort comprises: the method adopting cluster analysis, is divided to same cohort by user identical or similar for operating characteristics.
Preferably, the method for described cluster analysis is KMeans algorithm.
Preferably, described method also comprises: by predetermined report form, the identity belonging to user of each cohort that display divides and the user's ratio shared by each cohort of division.
The present invention not only overcomes the inefficiency manually determined one's identity, the shortcoming such as discrimination degree is low, renewal is difficult, but also compensate for the shortcoming that user is low from the participation of main separation identity process, confidence level is poor.
Accompanying drawing explanation
Fig. 1 is the process flow diagram of the method for the determination user identity illustrated according to exemplary embodiment of the present invention;
Fig. 2 is the model schematic of the digraph of the operation of certain user illustrated according to exemplary embodiment of the present invention;
Fig. 3 is the cluster schematic diagram of each cohort of the division illustrated according to exemplary embodiment of the present invention.
Embodiment
Below, embodiments of the invention are described in detail with reference to accompanying drawing.
Fig. 1 shows a kind of process flow diagram determining the preferred embodiment of the method for user identity of the present invention.
With reference to Fig. 1, in step 110, obtain the daily record data relevant to user operation.
Usually, the daily record run in various equipment is except recording the information such as various types of mistake (error), warning (warning), notice (info), debugging (debug), the network address (IP), URL(uniform resource locator) (URL), client type, thread number, filename, line number, function name, also may record the various information relevant with user operation, such as, the running time of the mark (ID) of user, the action type of user, the operation mark (ID) of user and user.
In order to carry out data analysis to the behavior of various user, by obtaining the daily record data relevant to user operation, obtain the operation information of user.
During concrete enforcement, by specific API (Application Programming Interface, application programming interface) interface obtains the daily record data relevant to user operation, also directly from the journal file or database of assigned address, the daily record data relevant to user operation can be obtained.
In the step 120, according to the daily record data obtained, the operating characteristics of each user is determined.
In order to portray and reflect the behavioural characteristic of each user better, digraph structure can be adopted to describe the operation behavior of each user, in an exemplary enforcement, can from the daily record data obtained, extract the operation information (that is, the running time etc. of the mark (ID) of user, the action type of user, the operation mark (ID) of user and user) of each user; Based on the operation information extracted, build the digraph of the operation of each user; Then, from the digraph built, the feature of the digraph of the operation of each user is extracted; The feature of the digraph of the operation of each user extracted is defined as the operating characteristics of each user.
Consider that former and later two operations of same user exist certain close relation, therefore, build in the embodiment of digraph at one, the node corresponding with the operation information extracted can be determined for same user; Line between two nodes operation existing predetermined priority and perform relation is defined as directed edge, and the running time of this directed edge node comparatively early points to running time of this directed edge more late node; Based on the node determined and directed edge, build the digraph of the operation of this user.
In addition, in the above-described embodiments, also comprise: before the line operated between two nodes that there is predetermined successively execution relation is defined as directed edge, determine whether the operation between any two nodes exists predetermined priority execution relation for the node determined.During concrete enforcement, by setting the time threshold between former and later two operations, determine whether there is successively execution relation between these two operations.Such as, between any two nodes running time, interval was not more than first threshold time, there is predetermined priority and perform relation in the operation determining between described two nodes.
Should be appreciated that, the invention is not restricted to adopt digraph structure to describe the operation behavior of each user, other contributes to the data structure describing user operation behavior, also all can be applicable to the present invention.
In addition, also it should be noted that, the feature of the digraph extracted in the present invention can be the node number of the out-degree of digraph, the in-degree of digraph, the limit number of digraph and digraph, also can be the combination of these information.Wherein, the out-degree of digraph is the number of the directed edge (out-edge) from all nodes in this digraph; The in-degree of digraph is the number of the directed edge (in-edge) of all nodes entered in this digraph.
Below in conjunction with accompanying drawing, above-mentioned implementation process of the present invention is further described.
Fig. 3 shows the model schematic of the digraph of the operation of certain user according to exemplary embodiment of the present invention.Shown in figure 1, 2, 3, 4, 5 represent node (Vertex) numbering that each operation information of this user is corresponding respectively, wherein, the line that node 0 points to node 3 represents that the user operation of node 0 performs prior to the user operation of node 3, the line that node 1 points to node 2 represents that the user operation of node 1 performs prior to the user operation of node 2, the line that node 0 points to node 5 represents that the user operation of node 0 performs prior to the user operation of node 5, the line that node 5 points to node 4 represents that the user operation of node 5 performs prior to the user operation of node 4, the line that node 3 points to node 2 represents that the user operation of node 3 performs prior to the user operation of node 2.
Based on the information shown in Fig. 3, can construct the digraph G (V, E) of the operation of this user, this digraph G (V, E) at least comprises set of node V (G) and directed edge E (G) collection, and is specifically expressed as follows:
V(G)＝{V
1，V
2，V
3，V
4，V
5}
E(G)＝{<V
0,V
3>，<V
1,V
2>，<V
0,V
5>，<V
5,V
4>，<V
3,V
2>}
Wherein, V
1, V
2, V
3, V
4, V
5represent each node in the digraph G (V, E) of the operation of this user respectively, <V
0, V
3>, <V
1, V
2>, <V
0, V
5>, <V
5, V
4>, <V
3, V
2> represents each directed edge in the digraph G (V, E) of the operation of this user respectively, wherein, and node V
0point to node V
3, node V
1point to node V
2, node V
0point to node V
5, node V
5point to node V
4, node V
3point to node V
2.
According to above-mentioned digraph, the out-degree (OD) of the limit number (Num_e) of the number of vertex (Num_v) of this digraph, digraph, the in-degree (ID) of digraph and digraph can be determined respectively:
Num_v(G)＝6
Num_e(G)＝5
ID(G)＝ID(V
0)+ID(V
1)+ID(V
2)+ID(V
3)+ID(V
4)＝5
OD(G)＝OD(V
0)+OD(V
1)+OD(V
2)+OD(V
3)+OD(V
4)＝5
Similarly, above-mentioned same method can be adopted, determine the operating characteristics of other users, then determine the operating characteristics of each user.
In step 130, according to operating characteristics, group is divided to user, wherein, user identical or similar for operating characteristics is divided to same cohort.
During concrete enforcement, the method for various existing cluster analysis (such as, KMeans clustering algorithm) can be adopted, user identical or similar for operating characteristics is divided to same cohort.
In step 140, the identity of a user in each cohort is obtained.
During concrete enforcement, based on the identity of known user, the identity belonging to cohort of this user can be determined.In an optional embodiment, by the authentication information of the existing user profile of Query Database or user, obtain the identity of a user in the same cohort divided.
In addition, also possible user identity can be listed by expert according to business experience, such as: supervisor, financial staff, sales force etc., then, part is randomly drawed (such as: user 1%) from all users, judge the identity belonging to these users according to expertise, and marked in attributive character corresponding to these users, for use in the identity of the user obtained in the same cohort of division.
In step 150, the identity of a described user is defined as the identity belonging to user of the cohort at a described user place.
Fig. 3 is the cluster schematic diagram of each cohort of the division illustrated according to exemplary embodiment of the present invention.Shown in figure, 301 is digraph corresponding to a user in the cohort of left side, and shown in figure, 302 is digraph corresponding to a user in the cohort of right side.When the identity of the user of digraph shown in 301 is for supervisor, can determine that the identity of left side shown in figure belonging to cohort is person in charge; Similarly, when the identity of the user of digraph shown in 302 is for selling, can determine that the identity of left side shown in figure belonging to cohort is sales force.
In addition, in the embodiment shown in fig. 1, described method also comprises: by predetermined report form (such as, various pie chart, histogram, Line Chart), the identity belonging to user of each cohort that display divides and the user's ratio shared by each cohort of division.The proportionate relationship of the people with different identity and the roughly formation of user can be understood by these forms.
Compared with prior art, the present invention not only overcomes the inefficiency manually determined one's identity, the shortcoming such as discrimination degree is low, renewal is difficult, also compensate for the shortcoming that user is low from the participation of main separation identity process, confidence level is poor simultaneously.In addition, utilize daily record to identify user identity, effectively can also detect the situation that an account is used by multiple user simultaneously.
Although with reference to preferred embodiment be and describe the present invention, it should be appreciated by those skilled in the art that when not departing from the spirit and scope of the present invention be defined by the claims, various change and conversion can be carried out to these embodiments.
Claims (9)
1. determine a method for user identity, it is characterized in that, comprising:
Obtain the daily record data relevant to user operation;
According to the daily record data obtained, determine the operating characteristics of each user;
According to operating characteristics, group is divided to user, wherein, user identical or similar for operating characteristics is divided to same cohort;
Obtain the identity of a user in each cohort;
The identity of a described user is defined as the identity belonging to user of the cohort at a described user place.
2. the method for claim 1, is characterized in that, the described daily record data relevant to user operation at least comprises following information:
The mark of user, the action type of user, the operation mark of user and the running time of user.
3. method as claimed in claim 2, is characterized in that, describedly determines that the step of the operating characteristics of each user comprises:
From the daily record data obtained, extract the operation information of each user;
Based on the operation information extracted, build the digraph of the operation of each user;
From the digraph built, extract the feature of the digraph of the operation of each user;
The feature of the digraph of the operation of each user extracted is defined as the operating characteristics of each user.
4. method as claimed in claim 3, it is characterized in that, the step of the digraph of the operation of each user of described structure comprises:
The node corresponding with the operation information extracted is determined for same user;
Line between two nodes operation existing predetermined priority and perform relation is defined as directed edge, and the running time of this directed edge node comparatively early points to running time of this directed edge more late node;
Based on the node determined and directed edge, build the digraph of the operation of this user.
5. method as claimed in claim 4, is characterized in that,
Between any two nodes running time, interval was not more than first threshold time, there is predetermined priority and perform relation in the operation determining between described two nodes.
6. method as claimed in claim 3, it is characterized in that, the feature of described digraph at least comprises one of following information:
The limit number of the out-degree of digraph, the in-degree of digraph, digraph and the node number of digraph.
7. the method for claim 1, is characterized in that, described step user identical or similar for operating characteristics being divided to same cohort comprises:
Adopt the method for cluster analysis, user identical or similar for operating characteristics is divided to same cohort.
8. method as claimed in claim 8, it is characterized in that, the method for described cluster analysis is KMeans algorithm.
9. the method as described in any one of claim 1 ~ 9, is characterized in that, also comprises:
By predetermined report form, the identity belonging to user of each cohort that display divides and the user's ratio shared by each cohort of division.
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| CN105224606A (en) * | 2015-09-02 | 2016-01-06 | 新浪网技术(中国)有限公司 | A kind of disposal route of user ID and device |
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| CN106815241A (en) * | 2015-11-30 | 2017-06-09 | 腾讯科技（北京）有限公司 | A kind of information processing method and terminal |
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| CN105227352B (en) * | 2015-09-02 | 2019-03-19 | 新浪网技术(中国)有限公司 | A kind of update method and device of user identifier collection |
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| CN107623715B (en) * | 2017-08-08 | 2020-06-09 | 阿里巴巴集团控股有限公司 | Identity information acquisition method and device |
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| CN110599278A (en) * | 2018-06-12 | 2019-12-20 | 百度在线网络技术（北京）有限公司 | Method, apparatus, and computer storage medium for aggregating device identifiers |
| CN110599278B (en) * | 2018-06-12 | 2022-07-22 | 百度在线网络技术（北京）有限公司 | Method, apparatus, and computer storage medium for aggregating device identifiers |
| CN109597844B (en) * | 2019-01-31 | 2020-12-22 | 中科人工智能创新技术研究院(青岛)有限公司 | Core user mining method and system based on deep neural network and graph network |
| CN109597844A (en) * | 2019-01-31 | 2019-04-09 | 中科人工智能创新技术研究院(青岛)有限公司 | Core customer's method for digging and system based on deep neural network Yu figure network |
| CN110929049A (en) * | 2019-12-02 | 2020-03-27 | 北京明略软件系统有限公司 | User account identification method and device |
| CN110929049B (en) * | 2019-12-02 | 2023-05-26 | 北京明略软件系统有限公司 | User account identification method and device |
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